Top Machine Learning Jobs in India

Develop AI strategies aligned with client business objectives, assess opportunities for AI-driven value, and lead project execution. Partner with data scientists, engineers, business analysts, and product teams to deliver enterprise AI transformations. Engage clients as a trusted advisor, communicate complex AI concepts to nontechnical stakeholders, and contribute thought leadership on emerging AI technologies and best practices.

Lead global MLOps delivery by designing scalable software architectures, data pipelines, and machine learning solutions. Guide architecture decisions, collaborate with research, architecture, and product teams, and coordinate software delivery. Manage and mentor developers, conduct code reviews, maintain documentation, promote engineering best practices, oversee testing and CI/CD, and remain accountable for technical debt and system quality.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

Build, tune, deploy, and validate machine learning models for fraud detection, risk scoring, and customer analytics in the BFSI and credit card domain. The role involves performance and stability testing, documentation, troubleshooting data and model issues, communicating findings to varied stakeholders, managing analytical projects independently, and staying current with emerging AI and ML techniques.

Leads the design, development, deployment, evaluation, and optimization of machine learning and deep learning models, including Generative AI and agentic systems. Builds scalable APIs and services, collaborates with data engineering and cross-functional teams, documents technical solutions, drives LLM adoption, and mentors junior engineers. Requires expertise in Python, ML/DL frameworks, data pipelines, MLOps, model evaluation, databases, and RESTful API development.

Leads AI and machine learning development initiatives, manages engineering teams, designs algorithms and models, mentors engineers, and oversees deployment of AI solutions. Requires an engineering degree and at least eight years of experience.

Lead the architecture and hands-on implementation of a data and AI platform spanning storage, pipelines, services, applications, and agentic systems. Design migration paths, data contracts, governance, lineage, infrastructure, APIs, and observability. Build production code, LLM systems, agentic tooling, MCP servers, evaluation harnesses, and reusable engineering standards. Make build-versus-buy decisions, resolve complex technical problems, mentor engineers, conduct reviews, and partner with product and business stakeholders.

Supports business intelligence projects involving SQL, Python, data analysis, ETL, data migration, reporting, dashboards, forecasting, and basic machine learning. The intern collaborates with BI, data engineering, product, and other teams to prepare data, generate insights, document work, and support business requirements while gaining hands-on experience with real business data.

Leads an enterprise fraud detection team responsible for identifying and preventing fraudulent activity, managing investigations, meeting service-level agreements, and maintaining quality and regulatory controls. Oversees hiring, coaching, staffing, performance management, training, process improvements, technology initiatives, and executive reporting. Partners with internal stakeholders and technical teams to improve fraud detection effectiveness, resolve sensitive issues, and drive scalable, compliant operations.

Leads integrated execution of complex, cross-functional AI programs from strategy and architecture through validation, deployment readiness, operationalization, and adoption. Owns program plans, milestones, dependencies, risks, decisions, technical gates, partner coordination, recovery planning, and executive reporting. Applies AI technical judgment across data science, software engineering, infrastructure, product, security, and operations. Establishes reusable practices for foundation models, observability, operational readiness, platform reuse, and strategic technology initiatives.

Supports Citi’s KYC and AML compliance program by creating, validating, updating, and completing KYC records. Reviews corporate and regulatory information, verifies ownership and management details, tracks workflow progress, meets service-level targets, escalates discrepancies and high-risk cases, maintains operational records, and reports metrics and issues to management. The role also assists with departmental projects and applies regulatory and internal policy requirements.

Reviews and improves commercial real estate data quality across layered data pipelines. Builds automated validation and AI-assisted anomaly detection checks, standardizes data definitions, tracks quality metrics, investigates root causes, and documents lineage. Analyzes property and market data to identify trends, creates dashboards and summaries, and communicates insights to brokers, researchers, and leadership. Partners with data and AI engineers to resolve upstream issues and recommends machine learning approaches for validation, deduplication, and document classification.

Design and deploy descriptive, predictive, and prescriptive analytics for supply chain planning. Build Python-based machine learning models for demand sensing, forecasting, anomaly detection, simulation, market intelligence, and optimization. Perform data collection, feature engineering, model tuning, validation, and exploratory analysis. Present insights, collaborate with clients and technical teams, and apply AI/ML methods to business problems using the o9 platform.

Lead technical direction and delivery for a core NIRA backend domain: design and ship high-volume web APIs, own architecture for distributed and queue-based systems, drive complex initiatives, mentor engineers, lead incident response, champion AI dev tools, and align cross-functionally to simplify systems and influence roadmap.

Supports Citi’s internal KYC program by creating, researching, validating, updating, and completing KYC records. Reviews entity ownership, management, formation documents, company news, and regulatory information; ensures compliance with local and global standards; tracks workflow status and operational metrics; meets service-level targets; escalates discrepancies, negative news, and high-risk cases; and communicates progress, issues, and reporting updates to supervisors and compliance teams.

Develop and deploy scalable machine learning solutions for industrial customers, from requirements gathering through production monitoring. Responsibilities include exploratory data analysis, feature engineering, Python development, model building, cloud deployment, MLOps, client presentations, technical collaboration, and mentoring junior data scientists. The role focuses on predictive quality, maintenance, energy reduction, and other AI applications in manufacturing and metal industries.

Analyze pricing, market, customer, and performance data to identify revenue opportunities and optimize pricing strategies. Build automated dashboards and analytical tools using large datasets across Databricks, BigQuery, and Looker. Apply regression, elasticity modeling, forecasting, machine learning, and scenario analysis to generate actionable pricing recommendations. Improve workflows, ensure data quality, train stakeholders, and influence commercial decisions across regions.

Configure and optimize SAP IBP modules for demand, supply, inventory, and S&OP planning. Responsibilities include designing forecasting models, planning areas, key figures, master data, MEIO parameters, dashboards, alerts, workflows, and integrations with SAP ECC or S/4HANA through CI-DS. The consultant will support enterprise supply chain planning, constraint-based capacity evaluations, and cross-functional S&OP processes.

Leads enterprise data strategy, roadmaps, governance, standards, architecture direction, and investment prioritization. Partners with senior stakeholders to improve data quality, security, privacy, stewardship, and platform capabilities. Evaluates modern data management technologies, champions AI and automation adoption, tracks KPIs and value realization, develops executive strategy materials, and mentors teams. Requires 10+ years of experience in data strategy, management, architecture, analytics, or a related field.

Develop and fine-tune machine learning, NLP, and deep learning models using Python and major ML frameworks. Work with transformer architectures and proprietary enterprise data, process documents using OCR and PDF parsing tools, and conduct rigorous model evaluation, statistical sampling, and error analysis.

Design, build, and operate production LLM systems for personalized multilingual learning. Responsibilities include developing RAG and agentic pipelines, backend services, data pipelines, evaluation frameworks, monitoring, and optimization for latency, cost, and reliability. The role integrates open-source and sovereign models into scalable production infrastructure and may include fine-tuning using LoRA, QLoRA, or SFT. Strong Python engineering, production ML systems experience, and cloud infrastructure knowledge are required.

Lead the design, development, optimization, and deployment of scalable machine learning pipelines and low-latency inference solutions. Build distributed data-processing and deep-learning workflows for forecasting and personalization using Python, Java, Scala, Spark, TensorFlow, and PyTorch. Implement real-time streaming, RESTful services, containerized deployments, model evaluation, statistical analysis, monitoring, and reproducible ML workflows. Collaborate with agile teams and work onsite at least four days weekly in Bangalore.

Develop quantitative models for cryptocurrency derivatives and spot markets, conduct statistical and econometric analysis, build pricing and risk models, and backtest systematic trading strategies. Collaborate with trading and product teams to validate and deploy models, monitor performance, analyze high-frequency data, and communicate research insights to leadership and trading desks.

Leads AI and machine learning projects by developing, training, deploying, optimizing, and collaborating on cognitive systems and machine learning models.

Provides AML consultation and analytics for Actimize customers. Analyzes financial data, investigates missed alerts, tunes transaction monitoring systems, and recommends segmentation, predictive scoring, and machine learning improvements. Prepares AML trend reports, translates business needs into technical requirements, manages stakeholder relationships, leads strategic initiatives, and mentors analytics team members. Requires strong knowledge of financial crime prevention, payment systems, AML regulations, and Actimize solutions.